# Towards Big data processing in IoT: network management for online edge data processing

## TL;DR

This paper proposes a Lyapunov optimization-based network management algorithm for MEC in IoT, balancing data processing delay and energy consumption for efficient big data handling at the edge.

## Contribution

It introduces a novel online resource management algorithm that jointly optimizes edge processing and transmission parameters without prior data distribution knowledge.

## Key findings

- The algorithm stabilizes data processing delay.
- It reduces energy consumption in edge networks.
- It effectively manages limited edge computing resources.

## Abstract

Heavy data load and wide cover range have always been crucial problems for internet of things (IoT). However, in mobile-edge computing (MEC) network, the huge data can be partly processed at the edge. In this paper, a MEC-based big data analysis network is discussed. The raw data generated by distributed network terminals are collected and processed by edge servers. The edge servers split out a large sum of redundant data and transmit extracted information to the center cloud for further analysis. However, for consideration of limited edge computation ability, part of the raw data in huge data sources may be directly transmitted to the cloud. To manage limited resources online, we propose an algorithm based on Lyapunov optimization to jointly optimize the policy of edge processor frequency, transmission power and bandwidth allocation. The algorithm aims at stabilizing data processing delay and saving energy without knowing probability distributions of data sources. The proposed network management algorithm may contribute to big data processing in future IoT.